JJ Lee
JJ Lee is a PhD candidate in the Robotic Exploration (REx) Lab at the Carnegie Mellon University Robotics Institute, where he works on dynamics modeling, optimization, and control for bio-inspired underwater robots. His research spans optimal control, fluid-structure interaction, and data-driven control methods for complex robotic systems.
Session
Matching the swimming efficiency and agility of fish has remained an elusive goal in underwater robotics — one that demands accurate simulation of complex vortex interactions between a robot's body and the surrounding fluid. These dynamics, governed by coupled ordinary and partial differential equations, pose far greater computational challenges than the multi-body dynamics of classical rigid robotic systems.
We present Aquarium 2.0, a Julia framework for simulating strongly coupled fluid-robot multiphysics as a unified optimization problem. The coupled manipulator and incompressible Navier-Stokes equations are derived together from a single Lagrangian using the principle of least action, and we employ discrete variational mechanics to obtain a stable, well-conditioned, and physically accurate scheme for jointly simulating articulated bodies and their surrounding fluid. Derivatives of the fully coupled dynamics are computed via the implicit function theorem, making the simulator directly amenable to gradient-based optimization within Julia's scientific computing ecosystem.
We showcase various swimming demonstrations of a bioinspired swimming robot, including forward undulation and a highly dynamic, optimized C-start escape maneuver. Both gaits are validated on physical hardware, demonstrating successful sim-to-real transfer.